How Redis achieves sub-millisecond latency - Visual Walkthrough

Concept Flow - How Redis achieves sub-millisecond latency

Client sends command

↓

Redis receives command

↓

Process command in memory

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Use single-threaded event loop

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Execute command quickly

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Send response back to client

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Client receives response

Redis processes commands in memory using a fast single-threaded event loop to respond quickly, minimizing delays.

Execution Sample

Redis

SET key1 value1
GET key1

Stores a value in memory and retrieves it immediately with minimal delay.

Execution Table

Step	Action	Internal Process	Latency Impact	Output
1	Client sends SET command	Command received by Redis server	Minimal network delay	None
2	Redis processes SET	Stores key1=value1 in memory	Very fast due to in-memory operation	OK
3	Client sends GET command	Command received by Redis server	Minimal network delay	None
4	Redis processes GET	Retrieves value1 from memory	Very fast due to in-memory operation	value1
5	Response sent to client	Data sent over network	Minimal network delay	value1

💡 Commands complete quickly because Redis uses in-memory storage and a single-threaded event loop minimizing processing overhead.

Variable Tracker

Variable	Start	After Step 2	After Step 4	Final
key1	undefined	value1 stored	value1 retrieved	value1
command_queue	empty	SET command processed	GET command processed	empty

Key Moments - 2 Insights

Why does Redis use a single-threaded event loop instead of multiple threads?

Using a single-threaded event loop avoids context switching and locking overhead, allowing Redis to process commands quickly and maintain sub-millisecond latency, as seen in steps 2 and 4 of the execution_table.

How does in-memory storage affect Redis latency?

Storing data in memory means Redis can read and write data instantly without disk delays, which is why steps 2 and 4 show very fast processing times.

Visual Quiz - 3 Questions

Look at the execution_table, what is the output after Redis processes the SET command?

A "value1"

B No output

C "OK"

D "ERROR"

💡 Hint

Check the Output column at Step 2 in the execution_table.

At which step does Redis retrieve the stored value from memory?

A Step 4

B Step 2

C Step 1

D Step 5

💡 Hint

Look at the Internal Process column for the GET command in the execution_table.

If Redis used disk storage instead of memory, how would the latency change in the execution_table?

A Latency would stay the same

B Latency would increase at steps 2 and 4

C Latency would decrease at steps 2 and 4

D Latency would increase only at step 5

💡 Hint

Consider the Latency Impact column for in-memory operations in the execution_table.

Concept Snapshot

Redis achieves sub-millisecond latency by:
- Using in-memory data storage for instant access
- Processing commands with a single-threaded event loop
- Minimizing context switching and locking overhead
- Handling commands quickly and sending responses immediately
- Avoiding disk I/O delays for fast performance

Full Transcript

Redis achieves sub-millisecond latency by processing commands entirely in memory using a single-threaded event loop. When a client sends a command like SET or GET, Redis receives it quickly, processes it in memory without disk access, and sends the response back immediately. This approach avoids delays from disk I/O and multi-threading overhead, enabling very fast response times. The execution table shows each step from receiving commands to sending responses, highlighting minimal latency at each stage. Key points include Redis's use of in-memory storage and single-threaded processing to keep latency low and performance high.

Practice

1. What is the main reason Redis can respond to commands in less than a millisecond?

easy

A. It keeps all data in memory for fast access.

B. It uses multiple threads to process commands simultaneously.

C. It stores data on disk to avoid memory overload.

D. It compresses data before processing.

Solution

1. Step 1: Understand Redis data storage

   Redis stores data primarily in memory, which is much faster than disk storage.

2. Step 2: Connect memory storage to speed

   Accessing data in memory avoids slow disk reads, enabling sub-millisecond response times.

3. Final Answer:

   It keeps all data in memory for fast access. -> Option A

4. Quick Check:

   In-memory storage = fast response [OK]

Hint: Remember: Memory access is faster than disk access [OK]

Common Mistakes:

• Thinking Redis uses disk for main data storage
• Assuming multithreading speeds Redis up
• Believing data compression is the main speed factor

2. Which of the following Redis command processing methods helps achieve sub-millisecond latency?

easy

A. Processing commands in a single thread sequentially

B. Batch processing commands every second

C. Using multiple threads to handle commands concurrently

D. Delaying commands to optimize throughput

Solution

1. Step 1: Identify Redis command processing model

   Redis processes commands in a single thread to avoid context switching delays.

2. Step 2: Understand impact on latency

   Single-threaded processing prevents delays from thread management, keeping latency low.

3. Final Answer:

   Processing commands in a single thread sequentially -> Option A

4. Quick Check:

   Single-threaded = low latency [OK]

Hint: Single thread avoids delays from switching [OK]

Common Mistakes:

• Assuming Redis uses multiple threads for speed
• Thinking batch processing reduces latency
• Believing command delays improve response time

3. Given Redis processes commands in a single thread and keeps data in memory, what is the expected latency when running a simple GET command?

medium

A. Milliseconds due to network delays

B. Sub-millisecond due to in-memory and single-threaded processing

C. Several seconds due to disk access

D. Minutes because of command queuing

Solution

1. Step 1: Analyze Redis command execution

   Redis executes simple commands like GET in memory and single-threaded, minimizing delays.

2. Step 2: Consider network and processing speed

   Network latency is low and processing is fast, resulting in sub-millisecond response times.

3. Final Answer:

   Sub-millisecond due to in-memory and single-threaded processing -> Option B

4. Quick Check:

   In-memory + single thread = sub-ms latency [OK]

Hint: Simple commands + memory = very fast response [OK]

Common Mistakes:

• Confusing disk access latency with Redis memory speed
• Overestimating network delay impact
• Thinking command queuing causes long waits

4. A developer notices Redis commands are slower than expected. Which of these is a likely cause related to Redis design?

medium

A. Using multiple threads causing race conditions

B. Network latency unrelated to Redis design

C. Commands processed in a single thread causing delays

D. Data stored on disk instead of memory

Solution

1. Step 1: Identify Redis normal data storage

   Redis normally stores data in memory for speed; storing on disk slows it down.

2. Step 2: Understand impact of disk storage

   Disk access is slower, causing higher latency than expected.

3. Final Answer:

   Data stored on disk instead of memory -> Option D

4. Quick Check:

   Disk storage = slower Redis commands [OK]

Hint: Check if data is in memory, not disk [OK]

Common Mistakes:

• Blaming single-thread processing for slowness
• Ignoring disk vs memory storage difference
• Assuming network latency is always the cause

5. To maintain sub-millisecond latency under heavy load, which combination of Redis features is most effective?

hard

A. Batch processing commands every minute with disk storage

B. Using disk persistence, multi-threaded processing, and complex commands

C. Using in-memory data, single-threaded processing, and simple commands

D. Compressing data and delaying command execution

Solution

1. Step 1: Identify features that reduce latency

   In-memory data and single-threaded processing minimize delays; simple commands execute quickly.

2. Step 2: Evaluate other options

   Disk persistence and batch processing add delays; multi-threading adds complexity; compression and delays slow responses.

3. Final Answer:

   Using in-memory data, single-threaded processing, and simple commands -> Option C

4. Quick Check:

   Memory + single thread + simple commands = fastest [OK]

Hint: Combine memory, single thread, simple commands for speed [OK]

Common Mistakes:

• Choosing disk persistence for speed
• Thinking multi-threading always improves latency
• Believing batch processing reduces response time